20 June 2017

A couple of days ago, David Robinson published an article on the Stack Overflow blog with a very provocative title: Developers Who Use Spaces Make More Money Than Those Who Use Tabs. It uses the data from Stack Overflow developer survey to show that indeed, using spaces is associated with higher salaries, even when we account for experience level. So, should you start using spaces instead of tabs to increase your salary?

25 January 2016

Warning: contains spoilers

If you have seen the new addition to the Star Wars franchise - The Force Awakens - you have probably noticed some similarities to the plots of some of the earlier movies, especially Episode IV: A New Hope. Does the similarity in the story translate to similarity in the social network of the new film? I downloaded the movie script, extracted the social network of characters and compared it to the social networks from the earlier movies.

15 December 2015

Some of us are looking forward to Christmas, and some of us are looking forward to the
new film in the Star Wars franchise, The Force Awakens. Meanwhile, I decided to look at the
whole 6-movie cycle from a quantitative point of view and extract the Star Wars social networks,
both within each film and across the whole Star Wars universe.
Looking at the social network structure reveals some surprising differences between the
original trilogy and the prequels.

3 December 2015

In my previous blog post
I visualized data on James Bond films both with Google Charts and with ggplot2.
Because I skipped the code relating to ggplot2, here I'd like to
look in detail at how to use ggplot2 from F#.

Currently
ggplot2 is my go-to visualization library (unless
I need to embed a plot - check out the James Bond bubble chart!).
Here I summarize some of my experiences with using ggplot2 from F# through the RProvider.
I also put together
a simple wrapper around the most common
ggplot2 functions to simplify the usage.

18 November 2015

Earlier this week I read an interesting article on visualizing box office success of
James Bond films using R and ggplot2 by Christoph Safferling (
you can find it here).
The blog post shows how to pull information from Wikipedia and visualize
the budget, box office and rating of each film - all this using R.
While reading the blog post, I couldn't help wondering how would a similar
analysis look in F# using the HTML type provider from the
F# Data library.

15 December 2014

This blog post marks day 15 of the amazing F# Advent Calendar.
Christmas is getting closer - soon we will have time to
relax and perhaps read a nice book.
Do you know who wrote the classic Christmas story,
'A Christmas Carol'?
All sources claim it was Charles Dickens, but how can we be sure? I'll look
at how this book compares to other books he wrote in terms of the language
used in the books. I'll also analyse other classic works of literature from
the Victorian and Edwardian era and look at similarity of their
language. In the end, I'll try to find out if it really was Charles Dickens
who wrote 'A Christmas Carol'.

15 September 2014

For my academic research, I recently wrote a library in F# for
fitting basic Gaussian process
regression that I used to model time-series gene expression data.
I am releasing the code publicly as an
Ariadne package.
In its current state, you can use the library to model various time-series
data.

9 June 2014

Fans of different programming languages always argue about benefits of their language
of choice. It is difficult to use objective criteria in a debate like this. Terms like
'clarity' or 'maintainability' are too vague and subjective. What if we used some tools from network
science to compare projects written in different languages?

In this blog post I use network analysis to investigate how complex dependency graphs are and
if they differ between C# and F#.
It turns out that F# and C#
dependency networks have quite different structures and use
different local network patterns.
For example, I'll describe specific types of cyclic dependencies
that frequently appear only in C# projects.

12 May 2014

Have you ever wondered who you should follow on Twitter to get more interesting
F# content?
Recently I've written a chapter on social network analysis for the new
F# Deep Dives book. The chapter shows
how to download data on social connections from Twitter and how to do some
exploratory data analysis, such as finding accounts that people
find worth following.

I worked with a network around the
F# Software Foundation's account. What
emerged is a nice picture of how F# community looks on Twitter and which
users are the most central to the network.
Since the results are quite interesting, I'd like to share them with wider F# community.